biosppy.features.time_freq

biosppy.features.time_freq

This module provides methods to extract time-frequency features using discrete wavelet decomposition.

copyright:

  1. 2015-2026 by Instituto de Telecomunicacoes

license:

BSD 3-clause, see LICENSE for more details.

Functions

compute_wavelet([signal, wavelet, level])

Compute the approximation and highest detail coefficients of the signal using the discrete wavelet transform.

time_freq([signal, wavelet, level])

Compute statistical metrics over the signal discrete wavelet transform approximation and detail coefficients.
biosppy.features.time_freq.compute_wavelet(signal=None, wavelet='db4', level=5)[source]

Compute the approximation and highest detail coefficients of the signal using the discrete wavelet transform.

Parameters:

  • signal (array) – Input signal.

  • wavelet (str) – Type of wavelet.

  • level (int) – Decomposition level

Returns:

  • dwt_app (array) – Approximation coefficients.

  • dwt_det{level} (array) – Detail coefficients at the specified level.

biosppy.features.time_freq.time_freq(signal=None, wavelet='db4', level=5)[source]

Compute statistical metrics over the signal discrete wavelet transform approximation and detail coefficients.

Parameters:

  • signal (array) – Input signal.

  • wavelet (str) – Type of wavelet. Default is db4 (Daubechies 4).

  • level (int) – Decomposition level. Default is 5.

Returns:

  • dwt_app_{metric} (float) – Statistical metrics over the approximation coefficients.

  • dwt_det{level}_{metric} (float) – Statistical metrics over the detail coefficients at the specified level.

Notes

Check biosppy.signals.tools.signal_stats for the list of available metrics.